Machine Learning Enginee

Role Description The Machine Learning Engineer role is a part-time, remote position contributing to data-driven initiatives that support SPE UM SC’s programs and activities. Day-to-day tasks include designing and implementing machine learning models, performing data preprocessing and feature engineering, and applying pattern recognition techniques to analyze relevant datasets. The role involves experimenting with neural network architectures, evaluating model performance using statistical methods, and optimizing algorithms for accuracy and efficiency. The Machine Learning Engineer may also collaborate with committee members to translate data insights into actionable recommendations, prepare clear documentation, and support the development of tools or dashboards that enhance decision-making and member engagement.

Qualifications

• Candidates should possess strong foundations in Computer Science and Algorithms for designing, implementing, and optimizing machine learning solutions.
• Candidates should possess skills in Statistics and Pattern Recognition for analyzing data, identifying trends, and validating model performance.
• Candidates should possess knowledge of Neural Networks and modern machine learning frameworks for building and training predictive models.
• Candidates should possess practical experience with programming languages such as Python or R, and familiarity with libraries like TensorFlow, PyTorch, or scikit-learn.
• Candidates should possess the ability to work independently in a remote setting, manage time effectively, and communicate technical concepts clearly to non-specialist team members.
• A background in engineering, computer science, data science, or a related field is beneficial; prior exposure to energy or oil and gas topics is an added advantage but not required.